Big Data and analytics workloads represent a new frontier for organizations. Data is being collected from sources that did not exist 10 years ago. Mobile phone data, machine-generated data, and website interaction data are all being collected and analyzed. In addition, as IT budgets are already under pressure, Big Data footprints are getting larger and posing a huge storage challenge. This paper provides information on the issues that Big Data applications pose for storage systems and how choosing the correct storage infrastructure can streamline and consolidate Big Data and analytics applications without breaking the bank.

Big data and analytics is a rapidly expanding field of information technology. Big data incorporates technologies and practices designed to support the collection, storage, and management of a wide variety of data types that are produced at ever increasing rates. Analytics combine statistics, machine learning, and data preprocessing in order to extract valuable information and insights from big data.

If your business is like most, you are grappling with data storage. In an annual Frost & Sullivan survey of IT decision-makers, storage growth has been listed among top data center challenges for the past five years.2 With businesses collecting, replicating, and storing exponentially more data than ever before, simply acquiring sufficient storage capacity is a problem.
Even more challenging is that businesses expect more from their stored data. Data is now recognized as a precious corporate asset and competitive differentiator: spawning new business models, new revenue streams, greater intelligence, streamlined operations, and lower costs. Booming market trends such as Internet of Things and Big Data analytics are generating new opportunities faster than IT organizations can prepare for them.

Nimble Secondary Flash array represents a new type of data storage, designed to maximize both capacity and performance. By adding high-performance flash storage to a capacity-optimized architecture, it provides a unique backup platform that lets you put your backup data to work.
Nimble Secondary Flash array uses flash performance to provide both near-instant backup and recovery from any primary storage system. It is a single device for backup, disaster recovery, and even local archiving. By using flash, you can accomplish real work such as dev/test, QA, and analytics.
Deep integration with Veeam’s leading backup software simplifies data lifecycle management and provides a path to cloud archiving.

Applications are the engines that drive today’s digital businesses. When the infrastructure that powers those applications is difficult to administer, or fails, businesses and their IT organizations are severely impacted. Traditionally, IT assumed much of the responsibility to ensure availability and performance. In the digital era, however, the industry needs to evolve and reset the requirements on vendors.
HPE Nimble Storage has broken away from convention and transformed how storage is managed and supported with the HPE InfoSight predictive analytics platform. HPE engaged ESG to conduct a quantitative survey of the HPE Nimble Storage installed base, as well as non-HPE Nimble Storage customers, to better assess how HPE InfoSight positively impacts customer environments.
To find out more download this whitepaper today.

In-memory technology—in which entire datasets are pre-loaded into a computer’s random access memory, alleviating the need for shuttling data between memory and disk storage every time a query is initiated—has actually been around for a number of years. However, with the onset of big data, as well as an insatiable thirst for analytics, the industry is taking a second look at this promising approach to speeding up data processing.

"This research by Nimble Storage, a Hewlett Packard Enterprise Company, outlines the top five causes of application delays. The report analyzes more than 12,000 anonymized cases of downtime and slow performance. Read this report and find out:
Top 5 causes of downtime and poor performance across the infrastructure stack
How machine learning and predictive analytics can prevent issues
Steps you can take to boost performance and availability"

Understand how infrastructure complexity slows down data delivery and why flash storage alone addresses less than half of slow downs.
HPE makes the data center of the future available today. There are thousands of customers that can benefit from InfoSight predictive analytics within the HPE Nimble Storage and HPE 3PAR platforms. Enterprises depend on data to improve customer interaction, accelerate product development and run the back office.

Think of a wildfire that quickly spreads as it increases in speed and power. That is what is happening today as data growth increases the volume and management complexity of storage, backup and recovery. Now think of trying to stop that fire with a garden hose. Your traditional backup and recovery process is equally under-equipped to manage and facilitate operations that need more speed, efficiency, scalability and reliability to handle today’s 24/7, always-on environment. Here we examine the benefits of moving from a solution comprised of multiple point products to a holistic data protection platform designed to serve today’s enterprise.

Learn how predictive analytics and machine learning can help optimize application performance and meet the needs of the business with Nimble Storage Infosight.
This ESG report highlights:
• Optimal application performance and delivery is difficult to achieve in complex environments.
• Many IT infrastructure and operations teams are stretched to the breaking point.
• Predictive analytics and machine learning can be applied to great effect.

"This research by Nimble Storage, a Hewlett Packard Enterprise Company, outlines the top five causes of application delays. The report analyzes more than 12,000 anonymized cases of downtime and slow performance. Read this report and find out:
-Top 5 causes of downtime and poor performance across the infrastructure stack
-How machine learning and predictive analytics can prevent issues
-Steps you can take to boost performance and availability

Powerful data protection in a converged appliance that is easy to deploy and manage — at the lowest cost-to-protect.
The integrated appliance brings together protection storage and software, search, and analytics — plus simplified system management and cloud readiness. And, the IDPA System Manager, with its clean, intuitive interface, provides a comprehensive view of data protection infrastructure from a single dashboard.

What is a Data Lake?
Today’s organizations are tasked with managing multiple data types, coming from a wide variety of sources. Faced with massive volumes and heterogeneous types of data, organizations are finding that in order to deliver insights in a timely manner, they need a data storage and analytics solution that offers more agility and flexibility than traditional data management systems.
Data Lakes are a new and increasingly popular way to store and analyze data that addresses many of these challenges. A Data Lakes allows an organization to store all of their data, structured and unstructured, in one, centralized repository. Since data can be stored as-is, there is no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand.
Download to find out more now.

Organizations are collecting and analyzing increasing amounts of data making it difficult for traditional on-premises solutions for data storage, data management, and analytics to keep pace. Amazon S3 and Amazon Glacier provide an ideal storage solution for data lakes. They provide options such as a breadth and depth of integration with traditional big data analytics tools as well as innovative query-in-place analytics tools that help you eliminate costly and complex extract, transform, and load processes.
This guide explains each of these options and provides best practices for building your Amazon S3-based data lake.

Forward-looking organizations are looking to next-generation all-flash storage platforms to eliminate storage cost and performance barriers. Advancements in all-flash technology have led to remarkable priceperformance improvements in recent years. The latest all-flash solutions from HPE deliver breakthrough economics, speed and simplicity, while improving availability and data durability. All-flash storage can help you reduce TCO and boost the performance of traditional applications as well as accelerate the rollout of new initiatives like IoT, big data and analytics. But moving data to a new storage architecture introduces a variety of organizational and technical challenges.

According to many market research analysts, the global wireless access point (WAP) market is anticipated to continue its upward trajectory and to grow at an impressive compound annual growth rate (CAGR) of approximately 8% through 2020. Many enterprises are utilizing cloudcomputing technology for cost-cutting purposes, eliminating investments required for storage hardware and other physical infrastructures. With significant growth expected in Internet usage, particularly bandwidth consuming video traffic, WAP vendors need to enable their customers to monitor and improve device performance, improve end user experience, and enhance security. These customers include general enterprises that offer Internet access to patrons like airports, hotels, retail / shopping centers and so on. These external Internet access providers can differentiate themselves by offering optimum service through advanced network analytics, traffic shaping, application control, security capabilities and more.

This ESG Lab report presents the results of a mixed workload performance benchmark test designed to assess the real world performance capabilities of an IBM Storwize V7000 storage system and IBM x3850 X5 servers in a VMware-enabled virtual server environment.

Pure Storage, a pioneer in block-based flash arrays, has developed a technology called FlashBlade, designed specifically for file and object storage environments. With FlashBlade, IT teams now have a simple-to-manage shared storage solution that delivers the performance and capacity needed to bring Spark deployments on premise.
To help gain a deeper understanding of the storage challenges related to Spark and how FlashBlade addresses them, Brian Gold of Pure Storage sat down with veteran technology journalist Al Perlman of TechTarget for a far-reaching discussion on Spark trends and opportunities.

Deep learning opens up new worlds of possibility in artificial intelligence, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly, and legacy storage systems are not keeping up. Advanced AI applications require a modern all-flash storage infrastructure that is built specifically to work with high-powered analytics.

This new report from Dynatrace and O'Reilly Media details the 3 stages that businesses move through as they migrate to the cloud, and a winning strategy for each stage. It includes case studies that will show you how to tackle both technical and cultural challenges along the way.

The enormous volume, velocity and variety of data flooding the enterprise, along with the push for analytics and business intelligence, is creating a massive challenge that is overwhelming traditional storage approaches. As the demand for capacity continues to escalate, companies must be able to effectively and dynamically manage the storage supply, but also the demand for storage resources. The key is to optimize the infrastructure through standardization and virtualization, and replace manual tasks with policy-based automation.

Data is growing at amazing rates and will continue this rapid rate of growth. New techniques in data processing and analytics including AI, machine and deep learning allow specially designed applications to not only analyze data but learn from the analysis and make predictions.
Computer systems consisting of multi-core CPUs or GPUs using parallel processing and extremely fast networks are required to process the data. However, legacy storage solutions are based on architectures that are decades old, un-scalable and not well suited for the massive concurrency required by machine learning. Legacy storage is becoming a bottleneck in processing big data and a new storage technology is needed to meet data analytics performance needs.